Overview

Dataset statistics

Number of variables18
Number of observations10400
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory144.0 B

Variable types

DateTime2
Categorical5
Numeric11

Alerts

spotify_url has a high cardinality: 1309 distinct values High cardinality
song has a high cardinality: 1165 distinct values High cardinality
artist has a high cardinality: 601 distinct values High cardinality
id has a high cardinality: 1309 distinct values High cardinality
energy is highly correlated with loudnessHigh correlation
loudness is highly correlated with energyHigh correlation
acousticness is highly correlated with energyHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly correlated with energyHigh correlation
week_start is highly correlated with week_endHigh correlation
week_end is highly correlated with week_startHigh correlation
acousticness is highly correlated with danceability and 3 other fieldsHigh correlation
danceability is highly correlated with acousticness and 2 other fieldsHigh correlation
energy is highly correlated with acousticness and 3 other fieldsHigh correlation
instrumentalness is highly correlated with loudnessHigh correlation
key is highly correlated with acousticnessHigh correlation
loudness is highly correlated with energy and 2 other fieldsHigh correlation
tempo is highly correlated with acousticness and 2 other fieldsHigh correlation
valence is highly correlated with energyHigh correlation
instrumentalness has 5047 (48.5%) zeros Zeros
key has 1031 (9.9%) zeros Zeros

Reproduction

Analysis started2022-02-23 06:52:09.183624
Analysis finished2022-02-23 06:52:26.445494
Duration17.26 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

week_start
Date

HIGH CORRELATION

Distinct52
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size81.4 KiB
Minimum2021-02-12 00:00:00
Maximum2022-02-04 00:00:00
2022-02-23T08:52:26.530293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:26.629287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

week_end
Date

HIGH CORRELATION

Distinct52
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size81.4 KiB
Minimum2021-02-12 00:00:00
Maximum2022-02-04 00:00:00
2022-02-23T08:52:26.742488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:26.849294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

spotify_url
Categorical

HIGH CARDINALITY

Distinct1309
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size81.4 KiB
https://open.spotify.com/track/7hxHWCCAIIxFLCzvDgnQHX
 
52
https://open.spotify.com/track/2gMXnyrvIjhVBUZwvLZDMP
 
52
https://open.spotify.com/track/5nujrmhLynf4yMoMtj8AQF
 
52
https://open.spotify.com/track/5QO79kh1waicV47BqGRL3g
 
52
https://open.spotify.com/track/3U4isOIWM3VvDubwSI3y7a
 
52
Other values (1304)
10140 

Length

Max length53
Median length53
Mean length53
Min length53

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique478 ?
Unique (%)4.6%

Sample

1st rowhttps://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CS
2nd rowhttps://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CS
3rd rowhttps://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CS
4th rowhttps://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CS
5th rowhttps://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CS

Common Values

ValueCountFrequency (%)
https://open.spotify.com/track/7hxHWCCAIIxFLCzvDgnQHX52
 
0.5%
https://open.spotify.com/track/2gMXnyrvIjhVBUZwvLZDMP52
 
0.5%
https://open.spotify.com/track/5nujrmhLynf4yMoMtj8AQF52
 
0.5%
https://open.spotify.com/track/5QO79kh1waicV47BqGRL3g52
 
0.5%
https://open.spotify.com/track/3U4isOIWM3VvDubwSI3y7a52
 
0.5%
https://open.spotify.com/track/5uEYRdEIh9Bo4fpjDd4Na952
 
0.5%
https://open.spotify.com/track/0VjIjW4GlUZAMYd2vXMi3b52
 
0.5%
https://open.spotify.com/track/6cx06DFPPHchuUAcTxznu952
 
0.5%
https://open.spotify.com/track/6f3Slt0GbA2bPZlz0aIFXN52
 
0.5%
https://open.spotify.com/track/02MWAaffLxlfxAUY7c5dvx52
 
0.5%
Other values (1299)9880
95.0%

Length

2022-02-23T08:52:26.959430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://open.spotify.com/track/7hxhwccaiixflczvdgnqhx52
 
0.5%
https://open.spotify.com/track/3faj6o0nohqv8mc5ri6enp52
 
0.5%
https://open.spotify.com/track/2gmxnyrvijhvbuzwvlzdmp52
 
0.5%
https://open.spotify.com/track/6uellqglwmcvh1e5c4h7ly52
 
0.5%
https://open.spotify.com/track/45be4hxi0awgzxfztmp8jr52
 
0.5%
https://open.spotify.com/track/4iunztcvt9difysszvsnvs52
 
0.5%
https://open.spotify.com/track/2h1hypekgiqap439cx7gsx52
 
0.5%
https://open.spotify.com/track/2vxelyx666f8uxcj0dzf8b52
 
0.5%
https://open.spotify.com/track/21jgcnket2qwijldfupipb52
 
0.5%
https://open.spotify.com/track/7qehsqek33rtcfnt9pfqlf52
 
0.5%
Other values (1299)9880
95.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

song
Categorical

HIGH CARDINALITY

Distinct1165
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size81.4 KiB
Someone You Loved
 
52
Mood (feat. iann dior)
 
52
drivers license
 
52
When I See U
 
52
Circles
 
52
Other values (1160)
10140 

Length

Max length91
Median length14
Mean length18.13961538
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique419 ?
Unique (%)4.0%

Sample

1st rowdrivers license
2nd rowdrivers license
3rd rowdrivers license
4th rowdrivers license
5th rowdrivers license

Common Values

ValueCountFrequency (%)
Someone You Loved52
 
0.5%
Mood (feat. iann dior)52
 
0.5%
drivers license52
 
0.5%
When I See U52
 
0.5%
Circles52
 
0.5%
Shallow52
 
0.5%
Lemonade (feat. Gunna, Don Toliver & NAV)52
 
0.5%
Save Your Tears52
 
0.5%
Before You Go52
 
0.5%
Umsebenzi Wethu52
 
0.5%
Other values (1155)9880
95.0%

Length

2022-02-23T08:52:27.077372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
feat1947
 
5.6%
1648
 
4.8%
with682
 
2.0%
the666
 
1.9%
you641
 
1.9%
me477
 
1.4%
i366
 
1.1%
love339
 
1.0%
remix335
 
1.0%
lil284
 
0.8%
Other values (1769)27094
78.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

artist
Categorical

HIGH CARDINALITY

Distinct601
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size81.4 KiB
Drake
 
333
Olivia Rodrigo
 
222
The Weeknd
 
221
Juice WRLD
 
220
Doja Cat
 
216
Other values (596)
9188 

Length

Max length115
Median length11
Mean length16.24788462
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique146 ?
Unique (%)1.4%

Sample

1st rowOlivia Rodrigo
2nd rowOlivia Rodrigo
3rd rowOlivia Rodrigo
4th rowOlivia Rodrigo
5th rowOlivia Rodrigo

Common Values

ValueCountFrequency (%)
Drake333
 
3.2%
Olivia Rodrigo222
 
2.1%
The Weeknd221
 
2.1%
Juice WRLD220
 
2.1%
Doja Cat216
 
2.1%
Ed Sheeran199
 
1.9%
Justin Bieber192
 
1.8%
Pop Smoke180
 
1.7%
Billie Eilish170
 
1.6%
J. Cole150
 
1.4%
Other values (591)8297
79.8%

Length

2022-02-23T08:52:27.213243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de511
 
1.8%
the475
 
1.6%
dj437
 
1.5%
drake360
 
1.2%
mr319
 
1.1%
jazziq319
 
1.1%
sir299
 
1.0%
trill296
 
1.0%
929295
 
1.0%
busta295
 
1.0%
Other values (955)25454
87.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

streams
Real number (ℝ≥0)

Distinct9097
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38669.19096
Minimum16889
Maximum429149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size81.4 KiB
2022-02-23T08:52:27.359088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum16889
5-th percentile19487
Q123950
median29415.5
Q342691.25
95-th percentile88485.45
Maximum429149
Range412260
Interquartile range (IQR)18741.25

Descriptive statistics

Standard deviation26868.48151
Coefficient of variation (CV)0.694829161
Kurtosis24.88422033
Mean38669.19096
Median Absolute Deviation (MAD)7149
Skewness3.852420856
Sum402159586
Variance721915298.7
MonotonicityNot monotonic
2022-02-23T08:52:27.475924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
246654
 
< 0.1%
287614
 
< 0.1%
239754
 
< 0.1%
223674
 
< 0.1%
226094
 
< 0.1%
219204
 
< 0.1%
237194
 
< 0.1%
301854
 
< 0.1%
223044
 
< 0.1%
268324
 
< 0.1%
Other values (9087)10360
99.6%
ValueCountFrequency (%)
168891
< 0.1%
169191
< 0.1%
169501
< 0.1%
169791
< 0.1%
169892
< 0.1%
169991
< 0.1%
170201
< 0.1%
170311
< 0.1%
170761
< 0.1%
170841
< 0.1%
ValueCountFrequency (%)
4291491
< 0.1%
3924651
< 0.1%
3888181
< 0.1%
3399631
< 0.1%
2915451
< 0.1%
2893651
< 0.1%
2880091
< 0.1%
2806741
< 0.1%
2695701
< 0.1%
2687311
< 0.1%

id
Categorical

HIGH CARDINALITY

Distinct1309
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size81.4 KiB
2VxeLyX666F8uXCJ0dZF8B
 
52
5uEYRdEIh9Bo4fpjDd4Na9
 
52
7hxHWCCAIIxFLCzvDgnQHX
 
52
6f3Slt0GbA2bPZlz0aIFXN
 
52
3FAJ6O0NOHQV8Mc5Ri6ENp
 
52
Other values (1304)
10140 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique478 ?
Unique (%)4.6%

Sample

1st row7lPN2DXiMsVn7XUKtOW1CS
2nd row7lPN2DXiMsVn7XUKtOW1CS
3rd row7lPN2DXiMsVn7XUKtOW1CS
4th row7lPN2DXiMsVn7XUKtOW1CS
5th row7lPN2DXiMsVn7XUKtOW1CS

Common Values

ValueCountFrequency (%)
2VxeLyX666F8uXCJ0dZF8B52
 
0.5%
5uEYRdEIh9Bo4fpjDd4Na952
 
0.5%
7hxHWCCAIIxFLCzvDgnQHX52
 
0.5%
6f3Slt0GbA2bPZlz0aIFXN52
 
0.5%
3FAJ6O0NOHQV8Mc5Ri6ENp52
 
0.5%
45bE4HXI0AwGZXfZtMp8JR52
 
0.5%
2gMXnyrvIjhVBUZwvLZDMP52
 
0.5%
6cx06DFPPHchuUAcTxznu952
 
0.5%
0VjIjW4GlUZAMYd2vXMi3b52
 
0.5%
6ft4hAq6yde8jPZY2i5zLr52
 
0.5%
Other values (1299)9880
95.0%

Length

2022-02-23T08:52:27.846601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2vxelyx666f8uxcj0dzf8b52
 
0.5%
5nujrmhlynf4ymomtj8aqf52
 
0.5%
5ueyrdeih9bo4fpjdd4na952
 
0.5%
4iunztcvt9difysszvsnvs52
 
0.5%
2h1hypekgiqap439cx7gsx52
 
0.5%
7qehsqek33rtcfnt9pfqlf52
 
0.5%
6uellqglwmcvh1e5c4h7ly52
 
0.5%
02mwaafflxlfxauy7c5dvx52
 
0.5%
5qo79kh1waicv47bqgrl3g52
 
0.5%
3u4isoiwm3vvdubwsi3y7a52
 
0.5%
Other values (1299)9880
95.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

acousticness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct886
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2253694052
Minimum3.84 × 10-5
Maximum0.978
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size81.4 KiB
2022-02-23T08:52:27.957953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.84 × 10-5
5-th percentile0.00262
Q10.0222
median0.127
Q30.336
95-th percentile0.768
Maximum0.978
Range0.9779616
Interquartile range (IQR)0.3138

Descriptive statistics

Standard deviation0.2504157341
Coefficient of variation (CV)1.111134557
Kurtosis0.4691952894
Mean0.2253694052
Median Absolute Deviation (MAD)0.1147
Skewness1.218779074
Sum2343.841814
Variance0.06270803989
MonotonicityNot monotonic
2022-02-23T08:52:28.089895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.127110
 
1.1%
0.48780
 
0.8%
0.32171
 
0.7%
0.2167
 
0.6%
0.18566
 
0.6%
0.60465
 
0.6%
0.021262
 
0.6%
0.12261
 
0.6%
0.18160
 
0.6%
0.29759
 
0.6%
Other values (876)9699
93.3%
ValueCountFrequency (%)
3.84 × 10-510
0.1%
0.0002153
 
< 0.1%
0.0002691
 
< 0.1%
0.00027422
0.2%
0.0003162
 
< 0.1%
0.0003554
 
< 0.1%
0.0003611
 
< 0.1%
0.0004061
 
< 0.1%
0.00041911
0.1%
0.00052
 
< 0.1%
ValueCountFrequency (%)
0.97813
 
0.1%
0.9741
 
< 0.1%
0.9676
 
0.1%
0.9611
 
< 0.1%
0.9571
 
< 0.1%
0.9414
 
< 0.1%
0.93711
 
0.1%
0.93452
0.5%
0.9322
 
< 0.1%
0.9281
 
< 0.1%

danceability
Real number (ℝ≥0)

HIGH CORRELATION

Distinct495
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6979378846
Minimum0.161
Maximum0.971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size81.4 KiB
2022-02-23T08:52:28.226415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.161
5-th percentile0.423
Q10.603
median0.719
Q30.808
95-th percentile0.883
Maximum0.971
Range0.81
Interquartile range (IQR)0.205

Descriptive statistics

Standard deviation0.1405247155
Coefficient of variation (CV)0.201342725
Kurtosis-0.1654311236
Mean0.6979378846
Median Absolute Deviation (MAD)0.103
Skewness-0.6737793175
Sum7258.554
Variance0.01974719568
MonotonicityNot monotonic
2022-02-23T08:52:28.353209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.824130
 
1.2%
0.671117
 
1.1%
0.842109
 
1.0%
0.695100
 
1.0%
0.54897
 
0.9%
0.79894
 
0.9%
0.6590
 
0.9%
0.84188
 
0.8%
0.78988
 
0.8%
0.63287
 
0.8%
Other values (485)9400
90.4%
ValueCountFrequency (%)
0.1613
 
< 0.1%
0.2342
 
< 0.1%
0.2422
 
< 0.1%
0.2692
 
< 0.1%
0.2751
 
< 0.1%
0.281
 
< 0.1%
0.2821
 
< 0.1%
0.3081
 
< 0.1%
0.3091
 
< 0.1%
0.31119
0.2%
ValueCountFrequency (%)
0.9711
 
< 0.1%
0.9691
 
< 0.1%
0.9611
 
< 0.1%
0.9575
 
< 0.1%
0.9567
 
0.1%
0.95312
0.1%
0.9512
0.1%
0.9452
 
< 0.1%
0.941
 
< 0.1%
0.93526
0.2%

energy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct530
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5923300769
Minimum0.0474
Maximum0.959
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size81.4 KiB
2022-02-23T08:52:28.500177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0474
5-th percentile0.34
Q10.488
median0.5935
Q30.699
95-th percentile0.825
Maximum0.959
Range0.9116
Interquartile range (IQR)0.211

Descriptive statistics

Standard deviation0.1495455318
Coefficient of variation (CV)0.2524699278
Kurtosis-0.2697959834
Mean0.5923300769
Median Absolute Deviation (MAD)0.1055
Skewness-0.2050901265
Sum6160.2328
Variance0.02236386607
MonotonicityNot monotonic
2022-02-23T08:52:28.613889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.586119
 
1.1%
0.593106
 
1.0%
0.521103
 
1.0%
0.449101
 
1.0%
0.82595
 
0.9%
0.60989
 
0.9%
0.65587
 
0.8%
0.78486
 
0.8%
0.40584
 
0.8%
0.7282
 
0.8%
Other values (520)9448
90.8%
ValueCountFrequency (%)
0.04742
 
< 0.1%
0.1011
 
< 0.1%
0.11113
0.1%
0.1281
 
< 0.1%
0.131
 
< 0.1%
0.1371
 
< 0.1%
0.1551
 
< 0.1%
0.1581
 
< 0.1%
0.1591
 
< 0.1%
0.1616
0.1%
ValueCountFrequency (%)
0.9591
 
< 0.1%
0.94110
0.1%
0.9352
 
< 0.1%
0.93414
0.1%
0.9251
 
< 0.1%
0.9231
 
< 0.1%
0.9171
 
< 0.1%
0.9152
 
< 0.1%
0.91315
0.1%
0.9124
 
< 0.1%

instrumentalness
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct580
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03461241302
Minimum0
Maximum0.938
Zeros5047
Zeros (%)48.5%
Negative0
Negative (%)0.0%
Memory size81.4 KiB
2022-02-23T08:52:28.728777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.43 × 10-6
Q30.000389
95-th percentile0.312
Maximum0.938
Range0.938
Interquartile range (IQR)0.000389

Descriptive statistics

Standard deviation0.1352010721
Coefficient of variation (CV)3.906144077
Kurtosis21.30070163
Mean0.03461241302
Median Absolute Deviation (MAD)1.43 × 10-6
Skewness4.592899215
Sum359.9690954
Variance0.0182793299
MonotonicityNot monotonic
2022-02-23T08:52:28.838210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05047
48.5%
0.0012262
 
0.6%
0.019254
 
0.5%
7.92 × 10-553
 
0.5%
1.02 × 10-653
 
0.5%
0.00030753
 
0.5%
9.54 × 10-552
 
0.5%
0.0024452
 
0.5%
1.14 × 10-552
 
0.5%
1.24 × 10-552
 
0.5%
Other values (570)4870
46.8%
ValueCountFrequency (%)
05047
48.5%
1.01 × 10-66
 
0.1%
1.02 × 10-653
 
0.5%
1.03 × 10-66
 
0.1%
1.04 × 10-611
 
0.1%
1.09 × 10-61
 
< 0.1%
1.18 × 10-64
 
< 0.1%
1.22 × 10-62
 
< 0.1%
1.23 × 10-61
 
< 0.1%
1.24 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.9383
 
< 0.1%
0.9271
 
< 0.1%
0.9254
 
< 0.1%
0.9171
 
< 0.1%
0.90326
0.2%
0.8941
 
< 0.1%
0.8893
 
< 0.1%
0.88811
0.1%
0.8877
 
0.1%
0.8831
 
< 0.1%

key
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.415096154
Minimum0
Maximum11
Zeros1031
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size81.4 KiB
2022-02-23T08:52:28.934624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.751629193
Coefficient of variation (CV)0.69280934
Kurtosis-1.414435304
Mean5.415096154
Median Absolute Deviation (MAD)4
Skewness-0.02459958912
Sum56317
Variance14.0747216
MonotonicityNot monotonic
2022-02-23T08:52:29.028144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11704
16.4%
81134
10.9%
111052
10.1%
01031
9.9%
101018
9.8%
7894
8.6%
6733
7.0%
2698
6.7%
5670
 
6.4%
9570
 
5.5%
Other values (2)896
8.6%
ValueCountFrequency (%)
01031
9.9%
11704
16.4%
2698
6.7%
3327
 
3.1%
4569
 
5.5%
5670
 
6.4%
6733
7.0%
7894
8.6%
81134
10.9%
9570
 
5.5%
ValueCountFrequency (%)
111052
10.1%
101018
9.8%
9570
5.5%
81134
10.9%
7894
8.6%
6733
7.0%
5670
6.4%
4569
5.5%
3327
 
3.1%
2698
6.7%

liveness
Real number (ℝ≥0)

Distinct613
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1545553365
Minimum0.0146
Maximum0.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size81.4 KiB
2022-02-23T08:52:29.138647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0146
5-th percentile0.037795
Q10.0829
median0.111
Q30.172
95-th percentile0.405
Maximum0.97
Range0.9554
Interquartile range (IQR)0.0891

Descriptive statistics

Standard deviation0.1261537686
Coefficient of variation (CV)0.8162368988
Kurtosis6.170451538
Mean0.1545553365
Median Absolute Deviation (MAD)0.038
Skewness2.235865195
Sum1607.3755
Variance0.01591477333
MonotonicityNot monotonic
2022-02-23T08:52:29.265854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.101163
 
1.6%
0.12151
 
1.5%
0.111136
 
1.3%
0.106126
 
1.2%
0.303122
 
1.2%
0.109122
 
1.2%
0.105110
 
1.1%
0.129105
 
1.0%
0.112103
 
1.0%
0.124101
 
1.0%
Other values (603)9161
88.1%
ValueCountFrequency (%)
0.014615
0.1%
0.01761
 
< 0.1%
0.0192
 
< 0.1%
0.01944
 
< 0.1%
0.020829
0.3%
0.021511
 
0.1%
0.02384
 
< 0.1%
0.02411
 
< 0.1%
0.02474
 
< 0.1%
0.02511
 
< 0.1%
ValueCountFrequency (%)
0.972
 
< 0.1%
0.9221
 
< 0.1%
0.8542
 
< 0.1%
0.84715
0.1%
0.8441
 
< 0.1%
0.8264
 
< 0.1%
0.7923
0.2%
0.7661
 
< 0.1%
0.741
 
< 0.1%
0.7276
 
0.1%

loudness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1146
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.683171635
Minimum-24.279
Maximum-2.018
Zeros0
Zeros (%)0.0%
Negative10400
Negative (%)100.0%
Memory size81.4 KiB
2022-02-23T08:52:29.403426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-24.279
5-th percentile-13.334
Q1-9.072
median-7.222
Q3-5.655
95-th percentile-3.714
Maximum-2.018
Range22.261
Interquartile range (IQR)3.417

Descriptive statistics

Standard deviation2.93562707
Coefficient of variation (CV)-0.3820853171
Kurtosis1.63641736
Mean-7.683171635
Median Absolute Deviation (MAD)1.692
Skewness-1.0796239
Sum-79904.985
Variance8.617906295
MonotonicityNot monotonic
2022-02-23T08:52:29.540479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.42462
 
0.6%
-6.31257
 
0.5%
-7.2355
 
0.5%
-7.07955
 
0.5%
-6.36253
 
0.5%
-10.10953
 
0.5%
-4.20952
 
0.5%
-6.68552
 
0.5%
-6.952
 
0.5%
-7.84652
 
0.5%
Other values (1136)9857
94.8%
ValueCountFrequency (%)
-24.2791
 
< 0.1%
-21.1141
 
< 0.1%
-20.7771
 
< 0.1%
-20.4341
 
< 0.1%
-19.9436
 
0.1%
-19.6855
 
< 0.1%
-19.64631
0.3%
-18.3451
 
< 0.1%
-18.0648
 
0.1%
-18.0311
 
< 0.1%
ValueCountFrequency (%)
-2.0182
 
< 0.1%
-2.3811
 
< 0.1%
-2.4911
 
< 0.1%
-2.72422
0.2%
-2.74910
 
0.1%
-2.8146
0.4%
-2.8731
0.3%
-2.87611
 
0.1%
-2.88111
 
0.1%
-2.9063
 
< 0.1%

speechiness
Real number (ℝ≥0)

Distinct689
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1067948462
Minimum0.0232
Maximum0.622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size81.4 KiB
2022-02-23T08:52:29.648084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0232
5-th percentile0.031795
Q10.044975
median0.0644
Q30.127
95-th percentile0.342
Maximum0.622
Range0.5988
Interquartile range (IQR)0.082025

Descriptive statistics

Standard deviation0.09588905806
Coefficient of variation (CV)0.8978809513
Kurtosis2.593156157
Mean0.1067948462
Median Absolute Deviation (MAD)0.0277
Skewness1.787286303
Sum1110.6664
Variance0.009194711455
MonotonicityNot monotonic
2022-02-23T08:52:29.759087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.242126
 
1.2%
0.112107
 
1.0%
0.2101
 
1.0%
0.034188
 
0.8%
0.034782
 
0.8%
0.032472
 
0.7%
0.029972
 
0.7%
0.030971
 
0.7%
0.034570
 
0.7%
0.10270
 
0.7%
Other values (679)9541
91.7%
ValueCountFrequency (%)
0.023247
0.5%
0.02493
 
< 0.1%
0.02511
 
< 0.1%
0.02531
 
< 0.1%
0.025911
 
0.1%
0.02623
 
< 0.1%
0.02652
 
< 0.1%
0.02748
0.5%
0.02761
 
< 0.1%
0.02791
 
< 0.1%
ValueCountFrequency (%)
0.6221
 
< 0.1%
0.5831
 
< 0.1%
0.5642
 
< 0.1%
0.5262
 
< 0.1%
0.5071
 
< 0.1%
0.4911
 
< 0.1%
0.4891
 
< 0.1%
0.48729
0.3%
0.4721
 
< 0.1%
0.474
 
< 0.1%

tempo
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1099
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.3425192
Minimum61.311
Maximum207.553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size81.4 KiB
2022-02-23T08:52:29.878043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum61.311
5-th percentile80.87
Q199.996
median113.99
Q3129.895
95-th percentile166
Maximum207.553
Range146.242
Interquartile range (IQR)29.899

Descriptive statistics

Standard deviation24.24525953
Coefficient of variation (CV)0.2066195586
Kurtosis0.2830471497
Mean117.3425192
Median Absolute Deviation (MAD)14.942
Skewness0.5621327813
Sum1220362.2
Variance587.8326095
MonotonicityNot monotonic
2022-02-23T08:52:29.979282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113.00195
 
0.9%
112.99974
 
0.7%
113.01274
 
0.7%
93.00563
 
0.6%
112.99562
 
0.6%
112.00661
 
0.6%
120.03159
 
0.6%
99.04856
 
0.5%
112.00855
 
0.5%
113.00854
 
0.5%
Other values (1089)9747
93.7%
ValueCountFrequency (%)
61.3113
 
< 0.1%
65.1361
 
< 0.1%
65.184
 
< 0.1%
65.98813
0.1%
67.1963
 
< 0.1%
67.2381
 
< 0.1%
67.2891
 
< 0.1%
68.0042
 
< 0.1%
68.1015
 
< 0.1%
70.9561
 
< 0.1%
ValueCountFrequency (%)
207.5532
 
< 0.1%
205.8635
 
< 0.1%
202.8991
 
< 0.1%
201.7392
 
< 0.1%
194.7461
 
< 0.1%
192.09919
0.2%
191.93
 
< 0.1%
188.71
 
< 0.1%
187.8151
 
< 0.1%
185.9721
 
< 0.1%

time_signature
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.4 KiB
4
9801 
3
 
368
5
 
211
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
49801
94.2%
3368
 
3.5%
5211
 
2.0%
120
 
0.2%

Length

2022-02-23T08:52:30.099674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-23T08:52:30.164715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
49801
94.2%
3368
 
3.5%
5211
 
2.0%
120
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

valence
Real number (ℝ≥0)

HIGH CORRELATION

Distinct628
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4726255962
Minimum0.0362
Maximum0.969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size81.4 KiB
2022-02-23T08:52:30.262359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0362
5-th percentile0.12
Q10.31
median0.474
Q30.619
95-th percentile0.862
Maximum0.969
Range0.9328
Interquartile range (IQR)0.309

Descriptive statistics

Standard deviation0.2155203844
Coefficient of variation (CV)0.4560065857
Kurtosis-0.6786731463
Mean0.4726255962
Median Absolute Deviation (MAD)0.155
Skewness0.0732263228
Sum4915.3062
Variance0.04644903609
MonotonicityNot monotonic
2022-02-23T08:52:30.388034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.537105
 
1.0%
0.437105
 
1.0%
0.33193
 
0.9%
0.54392
 
0.9%
0.44691
 
0.9%
0.43682
 
0.8%
0.51480
 
0.8%
0.49476
 
0.7%
0.73273
 
0.7%
0.55373
 
0.7%
Other values (618)9530
91.6%
ValueCountFrequency (%)
0.03621
 
< 0.1%
0.03741
 
< 0.1%
0.03811
 
< 0.1%
0.03821
 
< 0.1%
0.03871
 
< 0.1%
0.03933
 
< 0.1%
0.039730
0.3%
0.04983
 
< 0.1%
0.05241
 
< 0.1%
0.05341
 
< 0.1%
ValueCountFrequency (%)
0.9691
 
< 0.1%
0.9671
 
< 0.1%
0.961
 
< 0.1%
0.95821
0.2%
0.9571
 
< 0.1%
0.9474
 
< 0.1%
0.9431
 
< 0.1%
0.94210
0.1%
0.93422
0.2%
0.93111
0.1%

Interactions

2022-02-23T08:52:24.627954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:11.449718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:12.697438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:14.191640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:15.566004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:16.742902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:17.940485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:19.277930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:20.506299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:21.990089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:23.317645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:24.733938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:11.565563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:12.790147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:14.307973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:15.663275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:16.844173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:18.063499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:19.387982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:20.603891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:22.117752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:23.436533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:24.842702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:11.668569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:12.915742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:14.429876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:15.764285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:16.951913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:18.183496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:19.493613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:20.701943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:22.242351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:23.552404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:24.970147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:11.788483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:13.042261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:14.565936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:15.883220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:17.086116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:18.317564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:19.608635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:20.814940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:22.377899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:23.680140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:25.081521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:11.896860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:13.158133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:14.679014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:15.996390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:17.198044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:18.434378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:19.712763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:20.920170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:22.487173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:23.788389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:25.189234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:12.009279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:13.265505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:14.795505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:16.100748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:17.305499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:18.549292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:19.810564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:21.308367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:22.592239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:23.901043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:25.310307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:12.122217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:13.393206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:14.926055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:16.211790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:17.408310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:18.672697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:19.926348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:21.426167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:22.707241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:24.017893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:25.426434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:12.239165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:13.496636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:15.059569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:16.325293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:17.510538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:18.796426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:20.038230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:21.527184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:22.817975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:24.138916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:25.528161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:12.363916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:13.817687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:15.184465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-02-23T08:52:21.628525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-02-23T08:52:24.260184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:25.637350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:12.478731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-02-23T08:52:15.321786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:16.535086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:17.715071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:19.029567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:20.273678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:21.737125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:23.057747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:24.387392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:25.752305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:12.589255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:14.073511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:15.449149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:16.647372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:17.826805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:19.161886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:20.402620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:21.864836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:23.187853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-23T08:52:24.520115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-02-23T08:52:30.496946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-23T08:52:30.678290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-23T08:52:30.862164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-23T08:52:31.059190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-23T08:52:25.970742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-23T08:52:26.301687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

week_startweek_endspotify_urlsongartiststreamsidacousticnessdanceabilityenergyinstrumentalnesskeylivenessloudnessspeechinesstempotime_signaturevalence
02021-02-122021-02-12https://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CSdrivers licenseOlivia Rodrigo962457lPN2DXiMsVn7XUKtOW1CS0.7210.5850.4360.000013100.105-8.7610.0601143.87440.132
12021-02-192021-02-19https://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CSdrivers licenseOlivia Rodrigo921827lPN2DXiMsVn7XUKtOW1CS0.7210.5850.4360.000013100.105-8.7610.0601143.87440.132
22021-02-262021-02-26https://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CSdrivers licenseOlivia Rodrigo851337lPN2DXiMsVn7XUKtOW1CS0.7210.5850.4360.000013100.105-8.7610.0601143.87440.132
32021-03-052021-03-05https://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CSdrivers licenseOlivia Rodrigo812307lPN2DXiMsVn7XUKtOW1CS0.7210.5850.4360.000013100.105-8.7610.0601143.87440.132
42021-03-122021-03-12https://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CSdrivers licenseOlivia Rodrigo737047lPN2DXiMsVn7XUKtOW1CS0.7210.5850.4360.000013100.105-8.7610.0601143.87440.132
52021-03-192021-03-19https://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CSdrivers licenseOlivia Rodrigo709677lPN2DXiMsVn7XUKtOW1CS0.7210.5850.4360.000013100.105-8.7610.0601143.87440.132
62021-03-262021-03-26https://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CSdrivers licenseOlivia Rodrigo673757lPN2DXiMsVn7XUKtOW1CS0.7210.5850.4360.000013100.105-8.7610.0601143.87440.132
72021-04-022021-04-02https://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CSdrivers licenseOlivia Rodrigo644937lPN2DXiMsVn7XUKtOW1CS0.7210.5850.4360.000013100.105-8.7610.0601143.87440.132
82021-04-092021-04-09https://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CSdrivers licenseOlivia Rodrigo615247lPN2DXiMsVn7XUKtOW1CS0.7210.5850.4360.000013100.105-8.7610.0601143.87440.132
92021-04-162021-04-16https://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CSdrivers licenseOlivia Rodrigo578557lPN2DXiMsVn7XUKtOW1CS0.7210.5850.4360.000013100.105-8.7610.0601143.87440.132

Last rows

week_startweek_endspotify_urlsongartiststreamsidacousticnessdanceabilityenergyinstrumentalnesskeylivenessloudnessspeechinesstempotime_signaturevalence
103902022-02-042022-02-04https://open.spotify.com/track/0IuVhCflrQPMGRrOyoY5RWshe's all i wanna beTate McRae362640IuVhCflrQPMGRrOyoY5RW0.0134000.6140.6440.00000720.1170-5.3720.0426160.03640.6510
103912022-02-042022-02-04https://open.spotify.com/track/2AAyBZmMVZSZfgzXRYJOWQemo girl (feat. WILLOW)Machine Gun Kelly285712AAyBZmMVZSZfgzXRYJOWQ0.0004060.4100.8810.00004320.9220-3.5020.0870165.00640.3570
103922022-02-042022-02-04https://open.spotify.com/track/10hMM5nsZQf66ldBlgWBfGAll For Us - from the HBO Original Series EuphoriaLabrinth, Zendaya2615910hMM5nsZQf66ldBlgWBfG0.0165000.3480.6520.00026430.3300-7.1450.093170.95640.1720
103932022-02-042022-02-04https://open.spotify.com/track/3o9kpgkIcffx0iSwxhuNI2Numb Little BugEm Beihold247433o9kpgkIcffx0iSwxhuNI20.3270000.7420.5270.00000080.2500-6.8920.076984.97440.6380
103942022-02-042022-02-04https://open.spotify.com/track/3Oww84xrmgjyr5J1ilOmAfDown Under (feat. Colin Hay)Luude246083Oww84xrmgjyr5J1ilOmAf0.0116000.3080.8600.000828110.2770-4.1110.1680171.83540.0387
103952022-02-042022-02-04https://open.spotify.com/track/3eRE1KwnUma75nu1HhoNY4Can We TalkTevin Campbell244993eRE1KwnUma75nu1HhoNY40.0312000.6600.6460.00000520.0330-9.1110.0426172.37640.6210
103962022-02-042022-02-04https://open.spotify.com/track/6EtKlIQmGPB9SX8UjDJG5sFormulaLabrinth241466EtKlIQmGPB9SX8UjDJG5s0.0448000.5740.6640.055300110.1460-6.0680.0409144.65440.2260
103972022-02-042022-02-04https://open.spotify.com/track/7nHBwafnRUfqyJgg53xiVYUbusuku BayizoloNtosh Gazi239927nHBwafnRUfqyJgg53xiVY0.0240000.8240.9230.00000000.0516-3.4680.0549113.00740.7510
103982022-02-042022-02-04https://open.spotify.com/track/3zpGLSQ8QbbUnNjweWPLMDDoja$NOT, A$AP Rocky237893zpGLSQ8QbbUnNjweWPLMD0.0725000.7530.7770.000714110.1250-5.7010.3200157.14840.6690
103992022-02-042022-02-04https://open.spotify.com/track/1iSiayhy8ewrs7Bb2g0du4StallingNasty C233301iSiayhy8ewrs7Bb2g0du40.2850000.7050.5930.00000070.0986-6.3130.189098.92140.3060